Xiaogang Hu, Nina L. Suresh, Cindy Xue, William Z. Rymer
Abstract
The extensor digitorum communis muscle plays an important role in hand dexterity during object manipulations. This multi-tendinous muscle is believed to be controlled through separate motoneuron pools, thereby forming different compartments that control individual digits. However, due to the complex anatomical variations across individuals and the flexibility of neural control strategies, the spatial activation patterns of the extensor digitorum communis compartments during individual finger extension have not been fully tracked under different task conditions. The objective of this study was to quantify the global spatial activation patterns of the extensor digitorum communis using high-density (7 × 9) surface electromyogram (EMG) recordings. The muscle activation map (based on the root mean square of the EMG) was constructed when subjects performed individual four finger extensions at the metacarpophalangeal joint, at different effort levels and under different finger constraints (static and dynamic). Our results revealed distinct activation patterns during individual finger extensions, especially between index and middle finger extensions, although the activation between ring and little finger extensions showed strong covariance. The activation map was relatively consistent at different muscle contraction levels and for different finger constraint conditions. We also found that distinct activation patterns were more discernible in the proximal-distal direction than in the radial-ulnar direction. The global spatial activation map utilizing surface grid EMG of the extensor digitorum communis muscle provides information for localizing individual compartments of the extensor muscle during finger extensions. This is of potential value for identifying more selective control input for assistive devices. Such information can also provide a basis for understanding hand impairment in individuals with neural disorders.
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 January 2020.
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